Method, apparatus, and system for simulating a particle transport and determining human dose in a radiotherapy

ABSTRACT

A method for simulating a particle transport may include recording transport paths of inputted particles and determining an uncertainty of each of lattice cells based on the transport paths of each batch of the inputted particles, a lattice cell being a qualified lattice cell if an uncertainty of the lattice cell does not exceed a first threshold; determining a standard-reaching rate of lattice cells in a region of interest (ROI), the ROI including at least one lattice cell, the standard-reaching rate of lattice cells in the ROI being equal to a ratio of the number of qualified lattice cells to a total number of lattice cells in the ROI; and if the standard-reaching rate of lattice cells in the ROI exceeds a second threshold, stopping inputting particles, and outputting the transport paths of the inputted particles.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/563,576, filed on Sep. 30, 2017, now U.S. Pat. No. 10,737,115, issuedon Aug. 11, 2020, which is a national phase entry of InternationalApplication No. PCT/CN2015/090289, filed on Sep. 22, 2015, which claimspriority of Chinese Patent Application No. 201510152240.8, filed on Apr.1, 2015. Each of the above-referenced applications is expresslyincorporated herein by reference to their entireties.

TECHNICAL FIELD

The present disclosure relates to radiotherapy techniques, and moreparticularly, methods and apparatuses for simulating a particletransport, methods for determining a human dose in a radiotherapy, andsystems for radiotherapy.

BACKGROUND

In the field of radiotherapy technique, methods for determining a dosedistribution in a human tissue may broadly include two categories,including a semi-empirical analytic method and a Monte Carlo method.

The semi-empirical analytic method may include a method based on anoff-axis ratio (OAR) empirical equation, a convolution/superpositionmethod based on a pencil beam kernel and a point kernel, etc. However,the semi-empirical analytic method may have a limited accuracy.

The Monte Carlo method may be not substituted due to a capability ofsolving complex problems (e.g., complex geometry, a complex arrangementof a radiation source, etc.). The Monte Carlo method may be used toestablish models relating to a physical process accurately with lessapproximation in a radiotherapy. The biggest disadvantage of the MonteCarlo method may be its large computational strength and time-consuming.

SUMMARY

The present disclosure may be used to solve how to improve a simulationefficiency of a particle transport in a radiotherapy.

To solve the above technical problem, a method for simulating a particletransport is provided. The method may be used to simulate an energydistribution of particles in a lattice cell. The method may include:

estimating a total number of incident particles required, generatingincident particles, and inputting particles in batches;

recording transport paths of inputted particles;

determining an uncertainty of each of lattice cells based on thetransport paths of each batch of the inputted particles, a lattice cellbeing a qualified lattice cell if an uncertainty of the lattice celldoes not exceed a first threshold;

determining a standard-reaching rate of lattice cells in a region ofinterest (ROI), the ROI including at least one lattice cell, thestandard-reaching rate of lattice cells in the ROI being equal to aratio of the number of qualified lattice cells to a total number oflattice cells in the ROI;

if the standard-reaching rate of lattice cells in the ROI exceeds asecond threshold, stopping inputting particles, and outputting thetransport paths of the inputted particles, or if the standard-reachingrate of lattice cells in the ROI does not exceed the second threshold,continuing inputting particles until the number of inputted incidentparticles reaches the total number of incident particles required.

In some embodiments, incident particles belonging to a same batch mayhave a similar energy, or a same type, or a similar energy as well as asame type.

In some embodiments, the inputting particles in batches may include:

inputting particles in batches according to different types of incidentparticles.

In some embodiments, the transport paths of the inputted particles mayinclude energy information relating to incident particles, speedinformation relating to incident particles, and other path informationrelating to incident particles, the speed information relating toincident particles including an incident direction of an incidentparticle, and the recording transport paths of the inputted particlesmay include:

designating a recorded transport path of a recorded particle as atransport path of an incident particle if energy information andincident direction information of the recorded particle are close tothose of the incident particle.

In some embodiments, the other path information relating to incidentparticles may include type information relating to incident particles,incident position information relating to incident particles, weightinformation relating to incident particles, and information relating tolattice cells that the inputted particles pass;

the information relating to lattice cells that the inputted particlespass may include energy distributions and uncertainties corresponding tolattice cells that the inputted particles pass.

In some embodiments, the method may further include: when a batch ofparticles are inputted:

if a particle enters a second importance lattice cell from a firstimportance lattice cell, splitting, according to a first probability,the particle in the batch of particles. Particles generated by theparticle splitting may have a decreased weight such that a total weightof the batch of particles remains unchanged. An importance level of thefirst importance lattice cell may be lower than that of the secondimportance lattice cell;

if a particle enters a fourth importance lattice cell from a thirdimportance lattice cell, eliminating, according to a second probability,the particle in the batch of particles. The particle not beingeliminated may have an increased weight such that the total weight ofthe batch of particles remains unchanged. An importance level of thethird importance lattice cell may be higher than that of the fourthimportance lattice cell.

In some embodiments, the first probability may be equal to a ratio ofthe importance level of the first importance lattice cell to theimportance level of the second importance lattice cell, and the secondprobability may be equal to a ratio of the importance level of thefourth importance lattice cell and the importance level of the thirdimportance lattice cell.

In some embodiments, an importance level of an importance lattice cellmay be set manually, or automatically according to information relatingto the importance lattice cell. The information relating to theimportance lattice cell may include an uncertainty or a physicalproperty of the importance lattice cell.

In some embodiments, the method may further include:

performing a dynamic denoising operation on an uncertainty of a dosedistribution relating to incident particles based on historicaltransport paths of inputted particles.

In some embodiments, the performing dynamic denoising operation on anuncertainty of a dose distribution relating to incident particles basedon the transport paths of the inputted particles may include:

determining a three-dimensional dose distribution of particles in alattice cell and an uncertainty corresponding to the three-dimensionaldose distribution of particles;

performing a filtering operation on the three-dimensional dosedistribution such that the filtered three-dimensional dose distributionis continuously derivative in three dimensions; and

determining an uncertainty corresponding to the filteredthree-dimensional dose distribution.

In some embodiments, the method may further include: importing ageometrical model, the geometrical model including a lattice cellrelating to a simulated object, a physical material, a weight of thelattice cell and/or a geometrical virtual section. The geometricalvirtual section may be used to define the physical materialcorresponding to the lattice cell to make the simulated objectcorresponding to the lattice cell including a homogenized material. Thetransport paths of the inputted particles may relate to the geometricalvirtual section.

To solve the above technical problem, a method for determining a humandose in a radiotherapy is provided. The method may include: determininga human dose in a radiotherapy based on an energy distribution ofparticles in a lattice cell determined according to the method forsimulating a particle transport as described above.

To solve the above technical problem, a particle transport simulationapparatus is provided. The apparatus may be used to simulate an energydistribution of particles in a lattice cell. The apparatus may include:a source processing module, a transport processing module, a noiseprocessing module, and an outputting module;

the source processing module may be configured to estimate a totalnumber of incident particles required, generate incident particles, andinput particles in batches;

the transport processing module may be configured to record transportpaths of inputted particles;

the noise processing module may be configured to:

determining an uncertainty of each of lattice cells based on thetransport paths of each batch of the inputted particles, a lattice cellbeing a qualified lattice cell if an uncertainty of the lattice celldoes not exceed a first threshold;

determining a standard-reaching rate of lattice cells in a region ofinterest (ROI), the ROI including at least one lattice cell, thestandard-reaching rate of lattice cells in the ROI being equal to aratio of the number of qualified lattice cells to a total number oflattice cells in the ROI; and

if the standard-reaching rate of lattice cells in the ROI exceeds asecond threshold, stopping inputting particles, and outputting thetransport paths of the inputted particles, or if the standard-reachingrate of lattice cells in the ROI does not exceed the second threshold,continuing inputting particles until the number of inputted particlesreaches the total number of incident particles required.

To solve the above technical problem, a system for radiotherapy isprovided. The system may include:

a particle transport simulation apparatus as described above, theparticle transport simulation apparatus being configured to simulate anenergy distribution of particles in a lattice cell;

a dose determination apparatus being configured to determine a humandose in a radiotherapy based on the energy distribution of particles inthe lattice cell determined by the particle transport simulationapparatus.

Beneficial effects of the present disclosure may include:

the present disclosure may estimate a total number of incident particlesrequired, and estimate a total number of particles required to betransported in a specific phantom based on a user's requirements foruncertainty, which may be designated as a total computation target.Thus, unnecessary particle transports may be reduced when a user'starget is reached. The number of transported particles may be reduced toimprove a simulation efficiency in some cases that the particletransport simulation cannot be truncated.

According to the present disclosure, the speed of simulating a particletransport may be improved. A computation target may be quickly reachedby processing and evaluating an uncertainty of particles beingtransported, and the particle transport simulation may be truncatedafter reaching the computation target. Thereby the user's requirementsfor uncertainty may be satisfied meanwhile the computation time isdecreased and the simulation efficiency is improved greatly.

According to the present disclosure, the number of sampled particles inregions of interest (ROIs) may be dynamically adjusted according to theuncertainty of particles being transported, and particles may betransported in batches under the premise of ensuring an overalluncertainty balance. Thus, the number of sampled particles andunnecessary particle transports may be reduced to save a large amount ofcomputation time.

According to some embodiments of the present disclosure, incidentparticles may be classified and processed separately using a particleassimilation parallel processing technique. The incident particles maybe classified according to energy and type of incident particles.Incident particles having a similar energy and a same type may beclassified into a same batch, such that parallel computing units maycomplete computation in close time, thereby the simulation speed ofparallel computing may be increased.

According to some embodiments of the present disclosure, a dynamicdenoising may be achieved by performing a filtering operation on a dosedistribution and uncertainties of particles after a simulation of eachbatch of particles. The balance of uncertainties may be improved, andthe uncertainties relating to computing points of all ROIs may bedecreased to an acceptable range.

According to some embodiments of the present disclosure, the homogeneityof incident particles may also be increased. Directions of incidentparticles may be homogenized to decrease uncertainties of sourceincident particles which may facilitate the particle dynamic denoising.

According to some embodiments of the present disclosure, virtualcollision reactions (virtual reactions) and real physical reactions(real reactions) may be sampled distinctively in the simulation of aparticle transport. Transport degrees of particles may be sampled, anddirections and energies of particles may be not sampled under thevirtual reactions. Directions and energies of particles may bere-sampled only under the real reactions. Thereby, the number of sampledparticles may be reduced. According to the present disclosure, thenumber of sampled particles may be reduced by applying the followingoperations, including: reducing the number of sampled energies anddirections for a virtual section in a particle transport; copying a pathof a same incident particle directly to avoid repeated sampling;truncating particles based on energy to reduce transports of low-energyparticles; and truncating particles based on weight to reduce transportsof low importance particles.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating an exemplary process for simulating aparticle transport according to some embodiments of the presentdisclosure;

FIG. 2 is a flowchart illustrating another exemplary process forsimulating a particle transport according to some embodiments of thepresent disclosure;

FIG. 3 is a flowchart illustrating another exemplary process forsimulating a particle transport according to some embodiments of thepresent disclosure;

FIG. 4 is a schematic diagram illustrating a section reaction ofparticles in a particle transport according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

In the field of radiotherapy, ray beams generated by various types oftherapy machine may need to be simulated, such as high-energy electronbeams generated by an accelerator, proton beams, heavy ion and photonbeams, cobalt-60 (Co) photon beams, and X-ray beams generated by anX-ray therapy apparatus. Ray beams generated by a simulation therapyapparatus may mainly output simulation information of particles, andrecord information relating to particles arriving at or passing througha geometrical space defined by a user. The information relating toparticles may include charge carried by a particle, energy, a position,a direction of a particle, and course marks relating to materials aparticle passing through.

A method for simulating a particle transport based on the Monte Carlomethod may need to input information relating to characteristics oftherapy beams, such as an energy spectrum distribution, angulardistribution, spatial distribution, etc., of particles in an incidentfield. According to the inputted information, transports of a largenumber of particles may be simulated based on Monte Carlo method.

A method for simulating particle transport using the Monte Carlo methodmay be used to simulate a therapy head of an accelerator. The method mayinclude:

firstly, establishing a user application for simulating a therapy headof an accelerator and sectional data relating to required particlesinteracting with a medium;

secondly, completing an input of a user, mainly including a geometricaldefinition relating to a component and module in the therapy head of theaccelerator, a definition relating to incident particle beams, and aselection of control parameters relating to the user application;

then, performing a simulation computing and analyzing a computingresult, and inputting the computing result as a source item fordetermining an absorbed dose of a phantom.

A particle simulation using the Monte Carlo algorithm may be accurate byapplying a random sampling method. However, it is generally known thatsampling characteristics of the Monte Carlo method may make it consumetime inherently, and a simulation accuracy may be improved by a largenumber of random number samplings.

According to some embodiments of the present disclosure, a method forsimulating a particle transport using the Monte Carlo method may reduceparticle samplings by an algorithm combining a particle numberestimation with a particle uncertainty, thus, greatly improving thespeed and efficiency of simulating a particle transport based on theMonte Carlo method.

In order to make purposes, features and effects of the presentdisclosure more apparent and easier to understand, exemplary embodimentsof the present disclosure may be described in detail with reference tothe drawings.

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure, but the present disclosure may be implementedotherwise than as described herein, thus the present disclosure is notlimited to the embodiments disclosed below.

Embodiment 1

A particle transport simulation using a Monte Carlo algorithm mayachieve an accurate computing by using a large number of randomsampling. Particularly, in order to satisfy a user's requirement for anuncertainty of a particle transport, it may need to simulate a largenumber of particles, which may consume time.

According to an embodiment of the present disclosure, a method forsimulating a particle transport is provided. The method may be used tosimulate an energy distribution of particles in a lattice cell. Theparticle samplings and computing time may be reduced by determining anuncertainty of a geometrical lattice cell, distinguishing an importancelevel of a lattice cell, and balancing uncertainties of lattice cells inan ROI based on the Monte Carlo algorithm.

As shown in FIG. 1 , the method for simulating a particle transport mayinclude:

in step S100, a total number of incident particles required may beestimated, incident particles may be generated, and particles may beinputted in batches.

The incident particles may be generated based on a source, and thesource may be a simulation result of a known radiation source using aMonte Carlo tool. The source may have a form of a phase space sourcebased on a phase space file.

The estimation of the number of incident particles may includedetermining a relationship between a particle number and an uncertaintyby pre-simulating different numbers of particles in a homogenizedphantom (e.g., a water phantom), a simulated human body, or a referencehuman body, and determining a mapping relationship between the particlenumber and the uncertainty by an interpolating or fitting operation, anddetermining an estimation result. The estimation result may be a totalnumber of incident particles in a particle transport simulation.

The inputted particles may be generated based on different types ofsources, such as a source of a photon type, a source of an electrontype, or a source of a proton type. In the present embodiment, a methodfor inputting particles may include:

classifying incident particles having the same type, a similar energy,or a similar energy as well as the same type, into the same category,and processing particles of the same category in batches based on eachcategory of incident particles;

inputting, based on a category and batch of incident particles,particles alternately in batches according to a distribution of sourcesgenerating the incident particles.

For example, mixed sources of a photon type and an electron-type may bedescribed as an example. The mixed sources may be divided into Mbatches. Each batch may have photons and electrons. A ratio of photonsto electrons in each batch may be constant. All batches may betransported until a computing relating to all the batches is completed.Each batch of the same type may also be divided into N sub-batchesaccording to energy.

In the present embodiment, incident particles may be classifiedaccording to energy and type. Particles having a similar energy as wellas the same type may be designated as a same sub-batch such that thewaiting time between multiple threads in a parallel computing may bereduced, thereby increasing a simulation speed of the parallelcomputing.

In the present embodiment, different types of particles may betransported according to a distribution of sources, such that anuncertainty of the simulation computing may be reduced, and an errorintroduced may be decreased while the speed of the parallel computing isimproved.

In some embodiments, the inputting method described above may beadaptively modified, such as only designating particles having a similarenergy as well as a same type as a same batch and inputting particles inbatches directly; or inputting particles generated by different sourcesdirectly and alternately, or inputting a part of particles directly, orinputting a part of particles after being optimized according to thepresent embodiment above, all of which may be implemented.

Referring back to FIG. 1 , the method for simulating a particletransport in this embodiment may further include:

In step S101, transport paths of inputted particles may be recorded.

Transport information of particles may be sampled according to reactiontype characteristics of different particles in a particle transportsimulation using the Monte Carlo method. As used herein, transportinformation being sampled relating to a particle may be referred to as atransport path. The transport path may be a set of informationdescribing a sampled physical reaction type of the particle. A basicphysical parameter and a model relating to a sampled physical reactiontype of a particle may be pre-stored in a Monte Carlo tool. Hence, abasic physical parameter and a model may be obtained according to a typeof a particle, an energy of a particle, a speed of a particle, aproperty of a material corresponding to the position of a particlelocated, such that a transport path of a particle may be determined.

The basic physical parameter above may include a differential scatteringsection of a physical reaction, an average free path of a physicalreaction, etc., and the model may include a photoelectric effect, aCompton scattering, a pair reaction, etc., for describing a photon.

In the present embodiment, the transport path may include allinformation relating to a sampled physical reaction type of a particle,such as: a basic physical parameter and a model of the sampled physicalreaction type, energy information of an incident particle, speedinformation of an incident particle, and other path information of anincident particle. The speed information of an incident particle mayinclude an incident direction of the incident particle. The other pathinformation of an incident particle may include type information of anincident particle, incident position information of an incidentparticle, weight information of an incident particle, information oflattice cells that an inputted particle pass, and uncertaintiescorresponding to lattice cells that the inputted particle pass.

Step S101 of the present embodiment for recording transport paths ofinputted particles may include sampling particles and storing thetransport paths of inputted particles based on sampled particles. When aparticle is sampled, a basic physical parameter and a model relating toa physical reaction type may be sampled. The recording of the transportpaths may also be simplified in the present embodiment when theinformation above is determined, including:

designating a recorded transport path of a recorded particle as atransport path of an incident particle if energy information andincident direction information of the recorded particle are close tothose of the incident particle.

In some other embodiments, a sampling result relating to a basicphysical parameter and a model of a physical reaction type and arecorded transport path of a recorded particle may be copied anddesignated as a transport path of an incident particle according toenergy information and incident direction information of an inputtedparticle are close to those of a recorded particle.

The simplified recording method in the present embodiment may copy arepeated path of a particle based on same incident particles (the sameincident particles may refer to particles having similar energyinformation and incident direction) to avoid repeated sampling, reducethe sampling number of overall particles, and improve a simulationefficiency of a Monte Carlo tool.

For a particle not copying a transport path directly, a sampling of theincident particle and a storage of a transport path of the incidentparticle may include determining information relating to a sampledphysical reaction described above based on a random number. Theinformation relating to the sampled physical reaction may be used tosimulate a particle transport based on the Monte Carlo method togenerate a transport path of the incident particle.

Based on the prior art, it may be known that a particle transportsimulation based on the Monte Carlo method needs to simulate a largenumber of particles to satisfy a requirement for uncertainty. Thus, thesimulation algorithm may consume much time. Referring back to FIG. 1 ,the present embodiment may reduce the number of sampled particles basedon a global uncertainty to ensure an equalization of the globaluncertainty according to following operations including:

in step S102, an uncertainty of each of lattice cells may be determinedbased on the transport paths of each batch of the inputted particles. Alattice cell may be a qualified lattice cell if an uncertainty of thelattice cell does not exceed a lattice cell threshold;

in step S103, a standard-reaching rate of lattice cells in a region ofinterest (ROI) may be determined. The ROI may include at least onelattice cell. The standard-reaching rate of lattice cells in the ROI maybe equal to a ratio of the number of qualified lattice cells to a totalnumber of lattice cells in the ROI.

The lattice cell threshold may be used to evaluate the standard-reachingrate of a lattice cell, such that the standard-reaching rate of thelattice cell may satisfy a predetermined requirement.

Steps S102 and S103 may be performed, after each batch of incidentparticles have been sampled and transport paths of each batch ofincident particles have been stored. An uncertainty of a lattice cellmay be used to evaluate an uncertainty of a geometric lattice cell thatundergoes a section reaction with particles. As a particle transportsimulation based on the Monte Carlo method may have differentrequirements for uncertainties, an evaluation of a standard-reachingrate in ROIs may be used to balance the above uncertainties based on abalance of a standard-reaching rate of each of the ROIs.

If a standard-reaching rate of lattice cells in an ROI exceeds a presetthreshold (the preset threshold may be referred to as an ROI thresholdin FIG. 1 ), the standard-reaching rate of lattice cells in the ROI maybe evaluated to satisfy the predetermined requirement. Then, the MonteCarlo simulation may be truncated, also referred to as stoppinginputting particles, and a local simulation result (including therecorded transport paths of the recorded particles) may be outputted.The above uncertainty evaluation of the ROI may balance the globaluncertainty and reduce unnecessary particle samplings and transportsunder satisfying a requirement for the global uncertainty, which maysave much time and greatly improve a Monte Carlo simulation efficiency.

In addition, the present embodiment may also relate to a determinationof an uncertainty of a lattice cell. The evaluation of an uncertainty ofa lattice cell may have multiple definitions based on differentuncertainty criteria. The present embodiment discloses the following twoevaluation ways for reference.

An evaluation way may include:

determining sectional data relating to an interaction between a particleand each lattice cell based on transport paths of each batch of inputtedparticles, the sectional data including an actual probability that theparticles undergo a reaction in the lattice cell;

comparing the actual probability that the particles undergo the reactionin the lattice cell with an expected probability that the particlesundergo the reaction to obtain an uncertainty of the lattice cell.

The uncertainty evaluation of the lattice cell described above may takea section reaction relating to particles as the standard. It is knownfrom the prior art that the section reaction relating to particles mayrefer to a probability that a particle reacts with a section of alattice cell. The reaction may include that energy of a particle isabsorbed by a lattice cell the particle passing. The reaction may bedifferent as different absorption capabilities of lattice cells.According to different particle reactions, an expected probability thata particle undergoes a reaction and an actual probability that theparticle undergoes the reaction in a lattice cell may be compared todetermine an uncertainty of a probability that the reaction happens(such as the difference between the expected probability and the actualprobability). An uncertainty of the lattice cell may be obtained basedon an evaluation function relating to an uncertainty of probabilitiesthat multiple reactions happen in the lattice cell. The evaluationfunction may include a sum function, a mean function, etc.

Another evaluation way may include:

determining a particle number density in a lattice cell based ontransport paths of each batch of inputted particles;

determining a distribution curve of a particle number density in arelevant lattice cell according to the lattice cell and a sequencerelating to the particle number density; and

determining an uncertainty of the lattice cell based on the distributioncurve.

The uncertainty evaluation of a lattice cell described above may take aparticle number density of the lattice cell as a standard. The particlenumber density of a lattice cell may refer to the number of incidentparticles passing through the lattice cell. After a batch of inputtedparticles finish to be transported, the particle number density in alattice cell may be determined based on transport paths of the batch ofinputted particles, and a corresponding relationship between a latticecell and a particle number density may be determined (a string ofdiscrete sequences denoted by two-dimensional coordinates). Adistribution curve of particle number densities relating to latticecells may be fitted and determined based on the correspondingrelationship. A difference of a particle number density relating to alattice cell may be determined by comparing a value of the particlenumber density relating to the lattice cell on the determineddistribution curve with a particle number density of the lattice cell inan actual transport, thereby an uncertainty of the lattice cell may bedetermined.

If a standard-reaching rate of lattice cells in each of ROIs does notsatisfy the preset threshold, the next batch of incident particles maybe continued to be generated and inputted, and steps S101 to S103 may berepeated until a standard-reaching rate of lattice cells in each of ROIssatisfies the preset threshold, or the number of inputted particlesreaches the estimated total number of incident particles. Then, a localsimulation result (including all recorded transport paths of recordedparticles) may also be outputted.

Embodiment 2

The present embodiment provides a method as shown in FIG. 2 forsimulating a particle transport based on the first embodiment, which maybe used to balance uncertainties according to a global uncertainty of asimulation. The sampling number of particles may be adjusted dynamicallyaccording to a distribution of the uncertainties. The method mayinclude:

steps S100 to S103, which may be performed according to Embodiment 1.

When a batch of incident particles are inputted, an inputted particlemay be processed as follows based on a transport process of incidentparticles:

in step S104, if the transport process of incident particles shows aparticle in the batch of particles entering a high importance latticecell from a low importance lattice cell, the particle entering the highimportance lattice cell may be split according to a first probability.Transport paths of particles generated by the particle splitting maycopy that of the particle before splitting, and weights of the particlesgenerated by the particle splitting may be decreased such that a totalweight of the batch of particles remains unchanged.

in step S105, if the transport process of incident particles shows aparticle in the batch of particles entering a low importance latticecell from a high importance lattice cell, the particle entering the lowimportance lattice cell may be eliminated according to a secondprobability. A transport path of the particle not eliminated may beunchanged, and a weight of the particle not eliminated may be increasedsuch that the total weight of the batch of particles remains unchanged.

For simplicity, in step S104 of FIG. 2 , the low importance lattice cellmay be defined as a first importance lattice cell, and the highimportance lattice cell may be defined as a second importance latticecell; in step S105, the high importance lattice cell may be defined as athird importance lattice cell, and the low importance lattice cell maybe defined as a fourth importance lattice cell. However, it should beappreciated that importance lattice cells from the first importancelattice cell to the fourth importance lattice cell may be notnecessarily different importance lattice cells, which are only relativeconcepts to distinguish importance levels of the lattice cells.Importance level scopes of the importance lattice cells from the firstimportance lattice cell to the fourth importance lattice cell mayoverlap.

An importance lattice cell and an importance level of the importancelattice cell may be set manually, or automatically according toinformation relating to the importance lattice cell. The informationrelating to the importance lattice cell may include an uncertainty or aphysical property of the importance lattice cell.

In some embodiments, an importance lattice cell and an importance levelof the importance lattice cell may be preset manually before aradiotherapy by an operator via using an application software of aradiotherapy system. For example, several regions that may have a tumorin an ROI may be set as importance lattice cells, and importance levels(e.g., specific values) of the importance lattice cells may be setrespectively.

An importance level of an importance lattice cell may also be setautomatically according to an uncertainty of a lattice cell. In general,the higher an uncertainty of a lattice cell is, the higher an importancelevel of the lattice cell may be.

In steps S104 and S105, the first probability may be equal to a ratio ofthe importance level of the first importance lattice cell to theimportance level of the second importance lattice cell, and the secondprobability may be equal to a ratio of the importance level of thefourth importance lattice cell to the importance level of the thirdimportance lattice cell.

In the present embodiment, a particle entering a high importance latticecell from a low importance lattice cell may be split according to thefirst probability that may increase the number of transported particlesdynamically, and decrease an uncertainty of a lattice cell with a higherimportance level, such that the simulation accuracy of a particletransport may be improved. The method as described above may balance anuncertainty of each of the ROIs in the first embodiment, such thatbatches of particle transports may be reduced and the simulationefficiency may be improved. A particle entering a low importance latticecell from a high importance lattice cell may be split according to thesecond probability which may decrease the number of transportedparticles dynamically on the premise of ensuring the simulation accuracyand further improve the simulation efficiency.

Embodiment 3

The present embodiment provides a method for simulating particletransport as shown in FIG. 3 based on the first embodiment. Aftersimulating transport paths of predetermined batches of particles, aglobal uncertainty may be denoised dynamically based on historicaltransport paths of inputted particles according to input particles inbatches, thereby reducing uncertainty in the particle transportsimulation, which may help to reduce the sampling number of particlesand improve the simulation efficiency. The method may include thefollowing steps:

steps S100 to S103, which may be performed according to Embodiment 1.

In the particle transport simulation, if the batch number of inputtedparticle reaches a predetermined number, the following steps may beperformed:

in step S106, a dynamic denoising operation may be performed on a dosedistribution relating to incident particles.

In the present embodiment, the dynamic denoising operation may include:

determining a three-dimensional curve of a dose distribution ofparticles in a lattice cell (also referred to as a three-dimensionaldose distribution of particles in a lattice cell) and an uncertaintycorresponding to the dose distribution. The dose distribution ofparticles in the lattice cell may be determined an energy distributionof particles obtained based on a simulation, specifically, an energydistribution of particles with unit mass.

performing a filtering operation on the three-dimensional curve suchthat the filtered three-dimensional curve is continuously derivative inthree dimensions;

determining an uncertainty corresponding to the filtered dosedistribution.

In step S107, the uncertainty of the dose distribution in each latticecell determined based on the dynamic denoising operation may beoutputted.

In some embodiments, the uncertainty of the dose distribution in eachlattice cell may be determined based on the three-dimensional curve ofthe dose distribution processed based on the filtering operation.

The filtering operation may eliminate noises such as burr on thethree-dimensional curve of the dose distribution, and smooth thethree-dimensional curve. Then an uncertainty of a dose distribution ofparticles in a lattice cell may be determined according to the smoothedthree-dimensional curve, which may denoising the particle transportsimulation integrally and may be helpful for a next particle transportsimulation.

Embodiment 4

The present embodiment provides a method for simulating a particletransport, which defines a geometrical model relating to a simulationobject (the simulation object may include a variety of therapyapparatuses in the field of radiotherapy, such as an accelerator therapyhead) in a simulation process. The present embodiment may include:

importing a geometrical model, and other simulation steps. The othersimulation steps may include any one of steps as described in Embodiment1, Embodiment 2, and Embodiment 3.

The geometrical model may include a lattice cell relating to a simulatedobject, a physical material, a weight of the lattice cell and/or ageometrical virtual section. The geometrical virtual section may be usedto define the physical material corresponding to the lattice cell tomake the simulated object corresponding to the lattice cell including ahomogenized material. The transport paths of the inputted particles mayrelate to the geometrical virtual section.

The present embodiment may relate to a geometrical virtual section. Thegeometrical virtual section may be a sampling probability that aparticle undergoes a section reaction including a real reaction and avirtual reaction. For a transport of a particle, the geometric virtualsection may be defined as Σ_(max), in one sampling, the probability thatthe particle undergoes a real reaction may be Σ_(r), and the probabilitythat the particle undergoes a virtual reaction may be Σ_(r)′, r=1˜R;wherein, r is a natural number representing sampling times of theparticle, R is a natural number greater than or equal to 1 representingthe specific number that the particle is sampled in limited times; andthe geometric virtual section Σ_(max) may satisfy following conditions:Σ_(max)=max(Σ₁,Σ₂, . . . ,Σ_(R-1),Σ_(R));Σ_(max)=Σ₁+Σ₁′=Σ₂+Σ₂′= . . . =Σ_(R)+Σ_(R)′;

A sampling probability of a section reaction may be a maximumprobability that a particle passes a lattice cell after the particlebeing sampled in limited times according to the conditions above. Whenthe conditions are satisfied, all materials may use a geometric virtualsection Σ_(max) for performing transport length samplings under aparticle transport simulation on the whole model. The probability thateach material undergoes a real reaction is Σ_(r)/Σ_(max), and theprobability that each material undergoes a virtual reaction isΣ_(r)′/Σ_(max). Thus, a high consistency with a real physical transportmay be maintained when using a virtual section transport.

Referring to FIG. 4 , FIG. 4 is a schematic diagram of a sectionreaction in a particle transport. The arrow direction is an incidentdirection of a particle. A white circle represents a geometrical latticecell. A black circle represents a position where a particle undergoes avirtual reaction (virtual collision), and a shadow circle represents aposition where a particle undergoes a real reaction (real physicalreaction, such as a photoelectric effect and a Compton effect). Thetransport path of the particle is 1-2-3-4-5-6-7-8, wherein, 1, 5, 8represent positions of the real reaction, and 2, 3, 4, 6, 7 representpositions of the virtual reaction. It may be seen that after the virtualcollision reaction happens, only re-samplings relating to the transportdegree are needed, re-samplings relating to the particle direction andenergy are not needed, and only in the real reaction, re-samplingsrelating to the particle direction and energy are needed.

Thus, the geometric virtual section in the present embodiment isdifferent from that in the prior art, which is defined by ways asfollows:

a particle may be sampled according to a sampling probability of thesection reaction to pass through the lattice cell;

the sampling probability of the section reaction may be the sum of asampling probability of the real reaction and a sampling probability ofthe virtual reaction;

a sampling when the particle happens the real reaction with the latticecell may include a sampling relating to a transport degree, a directionand an energy, while a sampling when the particle happens the virtualreaction with the lattice cell may only include a sampling relating totransport degree.

Embodiment 5

The present embodiment provides a method for determining a human dose ina radiotherapy, including:

simulating a particle transport to obtain an energy distribution ofparticles in a lattice cell; and

determining a human dose in a radiotherapy based on the energydistribution of particles in the lattice cell,

wherein, the particle transport may be simulated according to any one ofthe embodiments 1 to 4.

Embodiment 6

The present embodiment provides an apparatus for simulating a particletransport corresponding to Embodiment 1. The apparatus may be used tosimulate an energy distribution of particles in a lattice cell,including a source processing module, a transport processing module, anoise processing module, and an outputting module.

The source processing module may be configured to perform step S100.

The transport processing module may be configured to perform step S101.

The noise processing module may be configured to perform steps S102 andS103, and a local simulation result may be outputted by the outputtingmodule.

In other embodiments, the apparatus for simulating a particle transportmay also correspond to Embodiment 2. The difference from the presentembodiment is that the noise processing module may be configured toperform steps S104 and S105.

In other embodiments, the apparatus for simulating a particle transportmay also correspond to Embodiment 3. The difference from the presentembodiment is that the noise processing module may be configured toperform steps S106 and S107.

Embodiment 7

The present embodiment provides an apparatus for simulating a particletransport corresponding to the fourth embodiment. The apparatus may beconfigured to simulate an energy distribution of particles in a latticecell, comprising: a source processing module, a transport processingmodule, a noise processing module, an outputting module, an inputtingmodule and a geometrical processing module.

The source processing module may be configured to perform step S100.

The transport processing module may be configured to perform step S101.

The noise processing module may be configured to perform steps S102 andS103, and the outputting module may output a local simulation result;

The inputting module may be configured to import a geometrical model asdescribed in Embodiment 4 to the geometrical processing module.

Embodiment 8

The present embodiment provides a radiotherapy system, including:

a particle transport simulation apparatus being configured to simulatean energy distribution of particles in a lattice cell as described inconnection with the Embodiment 6 and Embodiment 7; and

a dose determination apparatus being configured to determine a humandose in a radiotherapy based on the energy distribution of particles inthe lattice cell determined by the particle transport simulationapparatus.

Although the present disclosure has been described above in preferredembodiments, they are not intended to limit the disclosure, and withoutdeparting from the spirit and scope of the disclosure, any skilled inthe art may use the methods and technical contents disclosed above tomake a possible change and modification. Therefore, for contents withoutdeparting from the technical solution of the present disclosure, any ofsimple modifications, equal variations and modifications made to theabove embodiments according to the technical spirit of the presentdisclosure are within the scope of the technical scope of the presentdisclosure.

We claim:
 1. A method for simulating a particle transport, the methodcomprising: obtaining transport paths of at least a portion of simulatedparticles; determining, based on the transport paths, an uncertainty ofeach of lattice cells in each of multiple regions of interest (ROIs),the uncertainty indicating whether a simulated particle undergoes areaction in each of the lattice cells; determining an evaluation resultassociated with the particle transport by evaluating, based on theuncertainty of each of the lattice cells, the particle transport in thelattice cells: and adjusting, based on the evaluation result, theparticle transport to equalize global uncertainties of the multiple ROIssuch that the global uncertainty of each of the multiple ROIscorresponding to the adjusted particle transport does not exceed athreshold.
 2. The method of claim 1, wherein the evaluation resultassociated with the lattice cells indicates whether a standard-reachingrate of the lattice cells in each of the multiple ROIs exceeds a secondthreshold, and the determining an evaluation result includes: for eachof the multiple ROIs, determining, based on the uncertainty of each ofthe lattice cells in the ROI, the standard-reaching rate of the latticecells in the ROI, the standard-reaching rate of lattice cells in the ROIbeing equal to a ratio of a count of qualified lattice cells in the ROIto a total count of the lattice cells in the ROI, the uncertainty ofeach of the qualified lattice cells in the ROI not exceeding a thirdthreshold.
 3. The method of claim 2, wherein the determining, based onthe uncertainty of each of the lattice cells in the ROI, thestandard-reaching rate of the lattice cells in the ROI includes:determining, based on the uncertainty of each of the lattice cells inthe ROI and the first threshold, the count of qualified lattice cells inthe ROI; and determining, based on the count of qualified lattice cellsin the ROI, the standard-reaching rate of the lattice cells in the ROI.4. The method of claim 2, wherein the third threshold is determinedbased on the standard-reaching rate of the lattice cells in the ROI suchthat the standard-reaching rate of the lattice cells in the ROI exceedsa fourth threshold.
 5. The method of claim 2, wherein the adjusting,based on the evaluation result, the particle transport includes: inresponse to determining that the standard-reaching rate of lattice cellsin the ROI exceeds the second threshold, stopping the particletransport; and outputting the transport paths of the at least a portionof simulated particles.
 6. The method of claim 2, wherein the adjusting,based on the evaluation result, the particle transport includes: inresponse to determining that the standard-reaching rate of lattice cellsin the ROI does not exceeds the second threshold, continuing to performthe particle transport until the standard-reaching rate of lattice cellsin the ROI exceeds the second threshold.
 7. The method of claim 1,wherein the evaluation result associated with the lattice cellsindicates whether each of the at least a portion of simulated particlesenters a second lattice cell with a second level from a first latticecell with a first level, the first level being lower than the secondlevel, and the determining an evaluation result includes: for each ofthe multiple ROIs, determining, based on the uncertainty of each of thelattice cells in the ROI and the transport paths of the at least aportion of simulated particles, a level of each of the lattice cells inthe ROI; and determining, based on the level of each of the latticecells in the ROI, the evaluation result.
 8. The method of claim 7,wherein the adjusting, based on the evaluation result, the particletransport includes; in response to determining that a simulated particleenters the second lattice cell from the first lattice cell, splitting,according to a first probability, the simulated particle to generatesplit simulated particles; and recording transport paths of the splitsimulated particles.
 9. The method of claim 8, wherein the firstprobability is equal to a ratio of the first level of the first latticecell to the second level of the second lattice cell.
 10. The method ofclaim 8, further comprising: decreasing a weight of each of the splitparticles relative to the simulated particle such that a total weight ofthe at least a portion of simulated particles remains unchanged.
 11. Themethod of claim 7, wherein the adjusting, based on the evaluationresult, the particle transport includes: in response to determining thata simulated particle enters the first lattice cell from the secondlattice cell, eliminating, according to a second probability, thesimulated particle; and recording transport paths of simulated particlesthat are not eliminated in the at least a portion of simulatedparticles.
 12. The method of claim 11, further comprising: increasingweights of the simulated particles that are not eliminated in the atleast a portion of simulated particles such that a total weight of theat least a portion of simulated particles remains unchanged.
 13. Themethod of claim 11, wherein the second probability is equal to a ratioof the level of the second lattice cell to the first level of the firstlattice cell.
 14. The method of claim 1, wherein the transport paths ofthe at least a portion of simulated particles include: energyinformation relating to the at least a portion of simulated particles,speed information relating to the at least a portion of simulatedparticles, the speed information relates to the at least a portion ofsimulated particles including an incident direction of a simulatedparticle, and path information relating to the at least a portion ofsimulated particles, and the obtaining transport paths of at least aportion of simulated particles includes: designating a recordedtransport path of a recorded particle as a transport path of a simulatedparticle, the recorded particle being identified based on energyinformation and incident direction information of the recorded particle.15. The method of claim 14, wherein the path information relating to theat least a portion of simulated particles includes: type informationrelating to the at least a portion of simulated particles, incidentposition information relating to the at least a portion of simulatedparticles, weight information relating to the at least a portion ofsimulated particles, and information relating to lattice cells that theat least a portion of simulated particles pass.
 16. The method of claim1, wherein the determining, based on the transport paths, an uncertaintyof each of the lattice cells in each of multiple regions of interest(ROIs) includes: for each of the lattice cells in each of multiple ROIs,estimating, based on the transport paths, a probability that the atleast a portion of simulated particles undergo a reaction in the latticecell; and determining the uncertainty of the lattice cell based on theestimated probability and an expected probability that the at least aportion of simulated particles undergo the reaction.
 17. The method ofclaim 1, wherein the determining, based on the transport paths, anuncertainty of each of the lattice cells in each of multiple regions ofinterest (ROIs) includes: estimating, based on the transport paths, aparticle density in each of the lattice cells based on the transportpaths; obtaining a relationship between each of the lattice cells and anexpected particle density in each of the lattice cells; determining,based on the relationship and each of the lattice cells, the expectedparticle density of each of the lattice cells; and determining theuncertainty of the lattice cell based on the expected particle densityand the particle density.
 18. The method of claim 1, further comprising:obtaining recorded transport paths of simulated particles in one or morelattice cells based on the adjusted particle transport; determining anenergy distribution of the simulated particles in the one or morelattice cells based on the recorded transport paths; and determining ahuman dose in radiotherapy based on the determined energy distribution.19. The method of claim 1, further comprising: for each of at least oneof the lattice cells, performing a dynamic denoising operation on theuncertainty of a lattice cell based on historical transport paths; anddetermining the evaluation result associated with the particle transportby evaluating, based on the denoised uncertainty of each of the at leastone lattice cell.
 20. The method of claim 19, wherein the performingdynamic denoising operation on the uncertainty of a lattice cellincludes: determining a three-dimensional dose distribution of thesimulated particles in the lattice cell and an uncertainty correspondingto the three-dimensional dose distribution of the simulated particles inthe lattice cell; performing a filtering operation on thethree-dimensional dose distribution such that the filteredthree-dimensional dose distribution is differentiable in threedimensions; determining an uncertainty corresponding to the filteredthree-dimensional dose distribution; and designating the uncertaintycorresponding to the filtered three-dimensional dose distribution as thedenoised uncertainty of the lattice cell.